Abstract
This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera. The algorithm exploits both photometric and polarimetric light information to produce more accurate and complete geometry. The polarimetric information allows us to
recover the azimuth angle of surface normals from each
video frame to facilitate dense reconstruction, especially at
textureless or specular regions. There are two challenges in
our approach: 1) surface azimuth angles from the polarization camera are very noisy; and 2) we need a near real-time
solution for SLAM. Previous successful methods on polarimetric multi-view stereo are offline and require manually
pre-segmented object masks to suppress the effects of erroneous angle information along boundaries. Our fully automatic approach efficiently iterates azimuth-based depth
propagations, two-view depth consistency check, and depth
optimization to produce a depthmap in real-time, where all
the algorithmic steps are carefully designed to enable a
GPU implementation. To our knowledge, this paper is the
first to propose a photometric and polarimetric method for
dense SLAM. We have qualitatively and quantitatively evaluated our algorithm against a few of competing methods,
demonstrating the superior performance on various indoor
and outdoor scenes